2) will take our jobs. Element of truth in terms of repetitive, boring work that will be replaced. They will fill in for retiring workers. Some new industries created by them. Believe there will be net creation of jobs.

3) current approaches will still work.

6 steps to the Monetization of IoT, Terry Hughes:

Digital native companies (Uber) vs. digitally transforming companies

also companies such as Kodak that didn’t transform at all (vs. Fujifilm, which has transformed).

Forbes: 84% of companies have failed with at least one transformation program. Each time you fail you lose 1/2 billion

steps:

1) devices with potential

2) cloud network communication

3) software distribution

4) partner and provider ecosystem

5) create a marketplace.

6) monetization of assets.

crazy example of software company that still ships packages rather than just download because of initial cost in new delivery system

3 big software challenges for digitally transforming company

fragmented silos of software by product, business unit & software

messy and complex distribution channels

often no link between software and the hardware that it relates to

importance of an ecosystem

Blackberry example of one that didn’t have the ecosystem

3rd parties will innovate and add value around a manufacturer’s core products

Spotify: their vision is they understand us better. Can correlate your activity on Apple Watch (such as spinning) & create a play list based on that)

FitBit: the photo will estimate your calorie content.

John Deere

ShotSpotter: the company that monitors gun shots

understanding customers & markets better than before:

Facebook: better at face recognition than we are. They can predict your IQ, your relationship status.

Lot of frightening, IMHO, examples of AI analyzing individuals and responding without consideration of ethics and privacy

3) improving operations and efficiency:

self-driving boats

drones

medicine through Watson

panel on IoT:

Don’t be afraid of the cloud

Ryan Cahalane, Colfax: prepare for big, start small and move fast. They had remarkable growth with switch to IoT. Not a digital strategy, but digital in everything they do. Have “connected welders,” for example.

Justin Hester, Hirotec: most importatnt strategic digital transformation decision your organization can make is the selection of a platform. The platform is the underlying digital thread that enables your team to meet the unique and chanding needs of your organization and to scale those solutions rapidly. “Assisted reality” in ThingWorx

Shane O’Callahan, TSM (Ireland): Make industrial automation equipment for manufacturing. Understanding your key value driver is where to start. Then start samll, scale fast and get a win!

Jeffrey Miller, PTC: Digital Transformation:

if you start with digital strategy you’re starting in wrong place Start with business strategy.

Couple with innovation vision merged with digital strategy. Add business use cases.

Jobs: it’s not how much you spend on R & D, but “about the people you have, you you’re dled, and how much you get it”

example of Bell & Howell towers to store online sales in WalMart stores for customer pickup: very expensive to send one to a store for salesperson to use in sales — now just use AR app to give realistic demo without expense.

service: poor documentation organization, wants accurate, relevant, onsite info for technician. Want to remove return visits because the repair wasn’t done 1st time, or there’s a new technician. Manuals in binders, etc. Instead, with AR, requirements are quick access to current info. Finally, a demo.

no longer OK to think of a future destination, builds inertia (“your main competitor”). Disruption may have already happened. Hard to sustain advantage due to pace of change. Must “embrace a pace of change”

combo of physical, human and digital — transforming all at once speeds change:

physical: been constrained by subtractive manufacturing, while nature improves via cell division (i.e., additive). “Adopt Mother Nature’s mindset.” — new additive aspects of Creo. Example of Triumph cycle sing-arm using additive. CREO uses AI to optimize performance: non-symmetrical design. Still need to use simulation tests: new intermittent, continuous style: they are doing new partnership with ANSYS (product simulation software), unified modeling and simulation with no gaps. Historically, simulation only used at end of design cycle, now can use it throughout the process: “pervasive simulation.”

finally, human: “Mother Nature designed ups to interface with the physical. How do we integrate with the digital? — Siri, Alexa, Cortna still too slow. Sight is our best bet. “Need direct pipeline to reality ” — that’s AR. “Smart, connected humans.” Sysmex: for medical lab analysis. Hospitals need real-time access to blood cell analysis. They have real-time calibration of analysis equipment. Also improving knowledge of the support techs, using AR and digital twins when repairs are needed.

Will help 2.5 billion workers become more productive

AR can project how a process is being programmed (gotta see this one. will try to get video).

All of their human/digital interface initiatives united under Vuforia. Already have 10,000 enterprises using it.

Factories are a new focus of PTC. 200 companies now using it in 800 factories. Examples from Woodward & Colfax. Big savings on new employee training.

Keynote: Prof. Linda Hill, HBS, “Collective Genius”:

Innovation= novel + useful

Example of Pixar: collective genius “filmmaking is a team sport.”

3 characteristics of creative organizations they looked at:

“creative abrasion” — diversity and debate

“creative agility” — quickly test the idea & get feedback. Experiment rather than run pilots, which often include politics

This is not going to be pleasant for many readers, but bear with me — IMHO, it’s important to the IoT’s survival.

As I’ve written before, I learned during my work on corporate crisis management in the 80’s and 90’s that there’s an all-too-frequent gulf between the public and engineers on fear. Engineers, as left-brained and logical as they come (or, in Myers-Briggs lingo, ISTJs, “logical, detached and detailed” and the polar opposite of ENFP’s such as me, ” caring, creative, quick and impulsive” ) are ideally-suited for the precision needs of their profession — but often (but not always, I’ll admit…) clueless about how the rest of us respond to things such as the Russian disruption of our sacred political institutions via Facebook or any of the numerous violations of personal privacy and security that have taken place with IoT devices lacking in basic protections.

Engineers are quick to dismiss the resulting fear because it isn’t logical. But, as I’ve written before, the fact fear isn’t logical doesn’t mean it isn’t really real for many people, and can cloud their thought processes and decision-making.

Even worse, it’s cumulative and can ensnare good companies as well as bad. After a while, all the privacy and security violations get conflated in their minds.

““Maybe someone dies in a terrorist attack coordinated on our tools. And still we connect people. The ugly truth is that we believe in connecting people so deeply that anything that allows us to connect more people more often is *de facto* good.”

Eventually he, begrudgingly, apologized, as did Mark Zuckerberg, but, IMHO that was just facesaving. Why didn’t anyone at Facebook demand a retraction immediately, and why did some at Facebook get mad not at Bosworth but instead at anyone who’d leak such information? They and the corporate culture are as guilty as Bosworth in my mind.

So why do I bring up the story about identifying the source of your ramen using AI, which was surely written totally innocently by a Google engineer who thought it would be a cute example of how AI can be applied to a wide range of subjects? It’s because I read it — with my antennae admittedly sharpened by all the recent abuses — as something that might have been funny several years ago but should have gone unpublished now in light of all the fears about privacy and security. Think of this little fun project the way a lot of the people I try to counsel on technology fears every day would have: you mean they now can and will find out where I get my noodles? What the hell else do they know about me, and who will they give that information to???

Again, I’m quite willing to admit I may be over-reacting because of my own horror about the nonchalance on privacy and security, but I don’t think so.

That’s why I’ll conclude this screed with a call for all IoT engineers to undergo mandatory privacy and security training on a continuing basis. The risk of losing consumer confidence in their products and services is simply too great for them to get off the hook because that’s not their job. If you do IoT, privacy and security is part of the job description.

I haven’t posted since the end of October, because I was totally absorbed in writing The Future is Smart, my book about IoT strategy, which will be released in August by AMACOM, the publishing wing of the American Management Association. A major theme of the book is that the IoT lifts what I term the condition of “Collective Blindness” that used to plague us before the advent of real-time data from sensors and the analytical software to interpret that data. Collective Blindness meant that we were frequently operating in figurative darkness, having to guess about how things worked or didn’t without direct observational data, which meant that we frequently didn’t learn about problems inside things until after the fact, which could mean costly (and sometimes fatal) corrective maintenance was all that was possible.

Those “things” unfortunately included the human body.

Usually the only way to uncover a problem inside our bodies pre-IoT was through costly pre-arranged tests at the doctor’s or a hospital. They could only provide a snapshot in time, documenting your body’s state at that precise moment (when, after all, you might be flat on your back wearing a johnny — not exactly representative of your actual condition as you go about your daily routine!). If you had no complaint warranting such a test, the condition might go undiagnosed until it was significantly worse (remember the contrast between prompt predictive maintenance of a jet turbine and costly emergency repairs when a disaster loomed?).

According to TechCrunch, Ballinger’s team had previously used the Watch “to detect an abnormal heart rhythm with up to a 97 percent accuracy, sleep apnea with a 90 percent accuracy and hypertension with an 82 percent accuracy when paired with Cardiogram’s AI-based algorithm.”

This is important for several reasons.

We’ve read for several years about single-purpose devices that might be able to diagnose diabetes and determine the need for insulin without painful pinpricks, but the Cardiograph research might show that simply harvesting enough data with a multi-purpose fitness device such as the Watch and being able to interpret it creatively with Artificial Intelligence would be enough. That’s the logical next step with the Health eHeart Study.

Given these two examples, one must ask, how many other health problems might be diagnosed in their earliest stages, which cures are most likely and least expensive, if routine monitoring through devices such as the Apple Watch become commonplace and the results are crunched with AI? In particular, this could be a key part of my SmartAging concept.

Exciting!

NB: I work part-time for The Apple Store, but am not privy to any strategy or inside information. These opinions are purely my own as an Apple Watch user.

The video is a must watch: the doctors seem truly amazed by its versatility and ease-of-use — not to mention it can be accessed instantly in a life-or-death situation. As one is quoted saying, “This blows up the entire ultrasound playing field.”

It won’t be on the market until next year, but the FDA has already approved the iQ for diagnosis in 13 applications. Even more amazing, due to advanced electronics, it uses a single probe instead of three, and can document conditions from the superficial to deep inside the body. The system fits in a pants pocket and simply attaches to the doctor’s smartphone.

As incredible as the iQ will be in the US, think of how it will probably bring ultrasound to developing nations worldwide for the first time!

Another video discusses the engineering, which reduced the entire bulky ultrasound machine to a far-less costly chip, (including a lot of signal processing and computational power) and capitalizes on technologies developed for consumer electronics. The approach doesn’t just equal the traditional piezioelectric technology, but surpasses it. with power that would cost more than $100,000 with a conventional machine.

In terms of manufacturing, Butterfly can use the same chip machines used to produce consumer goods such as smartphones, and can print nearly 100 ultrasound machines on less than one disk.

Brilliant!Crowd-funded (even better!) Mycroft brings the rich potential of open-source to the growing field of digital home assistants. I suspect it won’t be long until it claims a major part of the field, because the Mycroft platform can evolve and grow exponentially by capitalizing on the contributions of many, many people, not unlike the way IFTTT has with its crowd-sourced smart home “recipes.”

According to a fascinating ZD Net interview with its developer, Joshua Montgomery, his motivation was not profit per se, but to create a general AI intelligence system that would transform a start-up space he was re-developing:

“He wanted to create the type of artificial intelligence platform that ‘if you spoke to it when you walked in the room, it could control the music, control the lights, the doors’ and more.”

Mycroft

Montgomery wanted to do this through an open-source voice control system but for there wasn’t an open source equivalent to Siri or Alexa.After building the natural language, open-source AI system to fill that need (tag line, “An Artificial Intelligence for Everyone”) he decided to build a “reference device” as the reporter terms it (gotta love that techno speak. In other words, a hardware device that could demonstrate the system). That in turn led to a crowdsourced campaign on Kickstarter and Backerkit to fund the home hub, which is based on the old chestnut of the IoT, Raspberry Pi. The result is a squat, cute (looks like a smiley face) unit, with a high-quality speaker.

Most important, when the development team is done with the AI platform, Mycroft will release all of the Mycroft AI code under GPL V3, inviting the open-source community to capitalize and improve on it.That will place Mycroft squarely in the open-source heritage of Linux and Mozilla.

Among other benefits, Mycroft will use natural language processing to activate a wide range of online services, from Netflix to Pandora, as well as control your smart home devices.

Mycroft illustrates one of my favorite IoT Essential Truths: we need to share data, not hoard it. I don’t care how brilliant your engineers are: they are only a tiny percentage of the world population, with only a limited amount of personal experience (especially if they’re callow millennials) and interests. When you go open source and throw your data open to the world, the progress will be greater as will be the benefits — to you and humanity.